Viterbi algorithm on a hypercube: Concurrent formulation

The similarity between the Fast Fourier Transform and the Viterbi algorithm is exploited to develop a Concurrent Viterbi Algorithm suitable for a multiprocessor system interconnected as a hypercube. The proposed algorithm can efficiently decode large constraint length convolutional codes, using different degrees of parallelism, and is attractive for VLSI implementation

KL-Divergence Guided Two-Beam Viterbi Algorithm on Factorial HMMs

This thesis addresses the problem of the high computation complexity issue that arises when decoding hidden Markov models (HMMs) with a large number of states. A novel approach, the two-beam Viterbi, with an extra forward beam, for decoding HMMs is implemented on a system that uses factorial HMM to simultaneously recognize a pair of isolated digits on one audio channel. The two-beam Viterbi algorithm uses KL-divergence and hierarchical clustering to reduce the overall decoding complexity. This novel approach achieves 60% less computation compared to the baseline algorithm, the Viterbi beam search, while maintaining 82.5% recognition accuracy.Ope

Lossy compression of discrete sources via Viterbi algorithm

We present a new lossy compressor for discrete-valued sources. For coding a sequence $x^n$, the encoder starts by assigning a certain cost to each possible reconstruction sequence. It then finds the one that minimizes this cost and describes it losslessly to the decoder via a universal lossless compressor. The cost of each sequence is a linear combination of its distance from the sequence $x^n$ and a linear function of its $k^{\rm th}$ order empirical distribution. The structure of the cost function allows the encoder to employ the Viterbi algorithm to recover the minimizer of the cost. We identify a choice of the coefficients comprising the linear function of the empirical distribution used in the cost function which ensures that the algorithm universally achieves the optimum rate-distortion performance of any stationary ergodic source in the limit of large $n$, provided that $k$ diverges as $o(\log n)$. Iterative techniques for approximating the coefficients, which alleviate the computational burden of finding the optimal coefficients, are proposed and studied.Comment: 26 pages, 6 figures, Submitted to IEEE Transactions on Information Theor

Performance evaluation of interference cancellation techniques using adaptive antennas

Two array-based algorithms, which jointly exploit or compensate for the spatial and temporal characteristics of the propagation channel, are proposed for intercell interference suppression in UMTS scenarios. The first one is the array extension of the Viterbi algorithm and is referred to as Vector Viterbi algorithm (VVA). The second algorithm, known as filtered training sequence multisensor receiver (FTS-MR), belongs to a class of algorithms in which a narrowband beamformer is placed prior to the MLSE detector. In order to assess performance of the proposed schemes, a set of link-level computer simulations adopting FRAMES' proposal for UMTS air-interface as well as realistic channel models for third generation communication systems is provided, Simulation results reveal gains, in terms of C/I, of 7-10 dB for the VVA with respect to the conventional VA and even higher for the FTS-MR.Peer ReviewedPostprint (published version

Power Imbalance Detection in Smart Grid via Grid Frequency Deviations: A Hidden Markov Model based Approach

We detect the deviation of the grid frequency from the nominal value (i.e., 50 Hz), which itself is an indicator of the power imbalance (i.e., mismatch between power generation and load demand). We first pass the noisy estimates of grid frequency through a hypothesis test which decides whether there is no deviation, positive deviation, or negative deviation from the nominal value. The hypothesis testing incurs miss-classification errors---false alarms (i.e., there is no deviation but we declare a positive/negative deviation), and missed detections (i.e., there is a positive/negative deviation but we declare no deviation). Therefore, to improve further upon the performance of the hypothesis test, we represent the grid frequency's fluctuations over time as a discrete-time hidden Markov model (HMM). We note that the outcomes of the hypothesis test are actually the emitted symbols, which are related to the true states via emission probability matrix. We then estimate the hidden Markov sequence (the true values of the grid frequency) via maximum likelihood method by passing the observed/emitted symbols through the Viterbi decoder. Simulations results show that the mean accuracy of Viterbi algorithm is at least $5$\% greater than that of hypothesis test.Comment: 5 pages, 6 figures, accepted by IEEE VTC conference, Fall 2018 editio